import numpy as np
import torch
import torchvision
from torchvision import transforms


class Cutout(object):
    """Cutout data augmentation is adopted from https://github.com/uoguelph-mlrg/Cutout"""

    def __init__(self, length=16):
        self.length = length

    def __call__(self, img):
        """
        Args:
            img (Tensor): Tensor image of size (C, H, W).

        Returns:
            Tensor: Image with n_holes of dimension length x length cut out of it. 
        """
        h = img.size(1)
        w = img.size(2)

        mask = np.ones((h, w), np.float32)

        y = np.random.randint(h)
        x = np.random.randint(w)

        y1 = np.clip(y - self.length // 2, 0, h)
        y2 = np.clip(y + self.length // 2, 0, h)
        x1 = np.clip(x - self.length // 2, 0, w)
        x2 = np.clip(x + self.length // 2, 0, w)

        mask[y1: y2, x1: x2] = 0.

        mask = torch.from_numpy(mask)
        mask = mask.expand_as(img)
        img *= mask
        return img


transform_train = transforms.Compose([
    torchvision.transforms.ToPILImage(mode='RGB'),
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
])

transform_train.transforms.append(Cutout())

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
])
